SoroushVahidi/scidocs huggingface dataset:
LNSC: A Security Model for Electric Vehicle and Charging Pile Management Based on Blockchain Ecosystem
(2018)

The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.
Round-Robin Arbiter Design and Generation
(2002)

In this paper, we introduce a Round-robin Arbiter Generator (RAG) tool. The RAG tool can generate a design for a Bus Arbiter (BA). The BA is able to handle the exact number of bus masters for both on-chip and off-chip buses. RAG can also generate a distributed and parallel hierarchical Switch Arbiter (SA). The first contribution of this paper is the automated generation of a round-robin token passing BA to reduce time spent on arbiter design. The generated arbiter is fair, fast, and has a low and predictable worst-case wait time. The second contribution of this paper is the design and integration of a distributed fast arbiter, e.g., for a terabit switch, based on 2x2 and 4x4 switch arbiters (SAs). Using a .25µ TSMC standard cell library from LEDA Systems [10, 14], we show the arbitration time of a 256x256 SA for a terabit switch and demonstrate that the SA generated by RAG meets the time constraint to achieve approximately six terabits of throughput in a typical network switch design. Furthermore, our generated SA performs better than the Ping-Pong Arbiter and Programmable Priority Encoder by a factor of 1.9X and 2.4X, respectively.
Extending the Coverage of DBpedia Properties using Distant Supervision over Wikipedia
(2013)

DBpedia is a Semantic Web project aiming to extract structured data from Wikipedia articles. Due to the increasing number of resources linked to it, DBpedia plays a central role in the Linked Open Data community. Currently, the information contained in DBpedia is mainly collected from Wikipedia infoboxes, a set of subject-attribute-value triples that represents a summary of the Wikipedia page. These infoboxes are manually compiled by the Wikipedia contributors, and in more than 50% of the Wikipedia articles the infobox is missing. In this article, we use the distant supervision paradigm to extract the missing information directly from the Wikipedia article, using a Relation Extraction tool trained on the information already present in DBpedia. We evaluate our system on a data set consisting of seven DBpedia properties, demonstrating the suitability of the approach in extending the DBpedia coverage.
Online Tourism Communities on the Path to Web 2.0: An Evaluation
(2008)

In recent years a technological and sociological paradigm shift has taken place in the Internet that is often referred to as Web 2.0. Companies and individuals have started to adapt existing Web sites to the new standards and principles and created new types of Web services and communities. The tourism domain is no exception to this trend new tourism communities emerged and long-established ones integrated new features to keep up with this trend. In this paper we are evaluating eight tourism communities with respect to Web 2.0. Each community is evaluated based on a criteria catalogue that draws ideas from online community studies. The findings are discussed in the context of the tourist life cycle that is structured in a pre-trip, on-site and after-trip phase. The value for the traveller is highlighted for each phase and potential problems are discussed.
Wideband Circularly Polarized Patch Antennas on Reactive Impedance Substrates
(2011)

A reduced-size wideband single-feed circularly polarized patch antenna is introduced for telemetry applications in S-band around 2300 MHz. The proposed structure consists of a slot-loaded patch antenna printed over an optimized metamaterial-inspired reactive impedance substrate (RIS). We demonstrate, step by step, the main role of each antenna element by comparing numerically and experimentally the performance of various antenna configurations: antenna over a single- or dual-layer substrate, standard patch or slot-loaded patch, antenna with or without RIS. The final optimized structure exhibits an axial-ratio bandwidth of about 15% and an impedance bandwidth better than 11%, which is much wider than the conventional printed antenna on the same materials.
Magnetic Field Analysis of Halbach Permanent Magnetic Synchronous Machine
(2014)

A numerical study of PMSM based on finite element method for estimating the airgap magnetic field distribution considering stator slots effect is presented both for traditional radial magnet structure and Halbach magnet array topology. Magnetic field, flux, torque and the induction in the air gap are presented. The two machines structures performances were compared which put in view the modifications brought by these structures.. According to numerical calculations, and harmonic analysis, the PMSM with Halbach magnet array provides a higher torque and better performance than the classical radial PMSM.
Rice quality analysis using image processing techniques
(2014)

In agricultural industries grain quality evaluation is very big challenge. Quality control is very important in food industry because after harvesting, based on quality parameters food products are classified and graded into different grades. Grain quality evaluation is done manually but it is relative, time consuming, may be varying results and costly. To overcome these limitations and shortcoming image processing techniques is the alternative solution can be used for grain quality analysis. Rice quality is nothing but the combination of physical and chemical characteristics. Grain size and shape, chalkiness, whiteness, milling degree, bulk density and moisture content are some physical characteristics while amylose content, gelatinization temperature and gel consistency are chemical characteristics of rice. The paper presents a solution of grading and evaluation of rice grains on the basis of grain size and shape using image processing techniques. Specifically edge detection algorithm is used to find out the region of boundaries of each grain. In this technique we find the endpoints of each grain and after using caliper we can measure the length and breadth of rice. This method requires minimum time and it is low in cost.
The British Industrial Revolution in Global Perspective
(2011)

THE INDUSTRIAL REVOLUTION was a turning point in the history of the world and inaugurated two centuries of economic growth that have resulted in the high incomes enjoyed in developed countries today.1 Technological progress is the motor of economic growth, and the Industrial Revolution is defi ned by famous technological breakthroughs: machinery to spin and weave cotton, the use of coal to smelt and refi ne iron, and the steam engine.2 In the words of the schoolboy made famous by T. S. Ashton: ‘About 1760 a wave of gadgets swept over England’ (Ashton 1955: 42). The questions for today’s lecture are: How can we explain the technological breakthroughs of the Industrial Revolution? And, why did the Industrial Revolution happen in Britain, rather than France, the Netherlands, or China? These questions will be answered by developing these themes: in comparison with other countries, Britain had an unusual structure of wages and prices in the eighteenth century, and this structure of wages and prices was a major factor in explaining why the revolution happened in Britain. In addition Britain had an effective ‘innovation system’ based on a high
Stanley: The robot that won the DARPA Grand Challenge
(2006)

Sebastian Thrun1, Mike Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel Hoffmann1, Kenny Lau1, Celia Oakley1, Mark Palatucci1, Vaughan Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott Ettinger3, Adrian Kaehler3, Ara Nefian3, and Pamela Mahoney4
Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces
(1996)

Real-time robot motion planning using rasterizing computer graphics hardware. In Proc. OY82] C. O'D unlaing and C.K. Yap. A retraction method for planning the motion of a disc. A local approach for path planning of manipulators with a high number of degrees of freedom. a path generation algorithm into oo-line programming of airbus panels. Motion planning for many degrees of freedom-random reeections at c-space obstacles. REFERENCES 31 iterative collision checker. We observed no dramatic slowdown of the planner. A challenging research goal would now be to extend the method to dynamic scenes. One rst question is: How should a roadmap computed for a given workspace be updated if a few obstacles are removed or added? Answering this question would be useful to apply our method to scenes subject to small incremental changes. Such changes occur in many manufacturing (e.g., assembly) cells; while most of the geometry of such a cell is permanent and stationary, a few objects (e.g., xtures) are added or removed between any two consecutive manufacturing operations. Similar incremental changes also occur in automatic graphic animation. A second question is: How should the learning and query phase be modiied if some obstacles are moving along known trajectories? An answer to this question might consist of applying our roadmap method in the conngurationtime space of the robot Lat91]. The roadmap would then have to be built as a directed graph, since local paths between any two nodes must monotonically progress along the time axis, with possibly additional constraints on their slope and curvature to reeect bounds on the robot's velocity and acceleration. A motion planner with performance proportional to task diiculty. 30 In KL94a, KL94b, O S94] prior versions of the method have been applied to a great variety of holonomic robots including planar and spatial articulated robots with revolute, prismatic, and/or spherical joints, xed or free base, and single or multiple kinematic chains. In Sve93, SO94] a variation of the method (essentially one with a diierent general local planner) was also run successfully on examples involving nonholonomic car-like robots. Experimental results show that our method can eeciently solve problems which are beyond the capabilities of other existing methods. For example, for planar articulated robots with many dofs, the customized implementation of Section 5 is much more consistent than the Randomized Path Planner (RPP) of BL91]. Indeed, the latter can be very fast on some diicult problems, but it …
Mushroom immunomodulators: unique molecules with unlimited applications.
(2013)

For centuries, mushrooms have been used as food and medicine in different cultures. More recently, many bioactive compounds have been isolated from different types of mushrooms. Among these, immunomodulators have gained much interest based on the increasing growth of the immunotherapy sector. Mushroom immunomodulators are classified under four categories based on their chemical nature as: lectins, terpenoids, proteins, and polysaccharides. These compounds are produced naturally in mushrooms cultivated in greenhouses. For effective industrial production, cultivation is carried out in submerged culture to increase the bioactive compound yield, decrease the production time, and reduce the cost of downstream processing. This review provides a comprehensive overview on mushroom immunomodulators in terms of chemistry, industrial production, and applications in medical and nonmedical sectors.
One Road to Turnover: An Examination of Work Exhaustion in Technology Professionals
(2000)

The concept of work exhaustion (or job burnout) from the management and psychology research literature is examined in the context of technology professionals. Data were collected from 270 IT professionals and managers in various industries across the United States. Through structural equation modeling, work exhaustion was shown to partially mediate the effects of workplace factors on turnover intention. In addition, the results of the study revealed that: (1) technology professionals experiencing higher levels of exhaustion reported higher intentions to leave the job and, (2) of the variables expected to influence exhaustion (work overload, role ambiguity and conflict, lack of autonomy and lack of rewards), work overload was the strongest contributor to exhaustion in the technology workers. Moreover, exhausted IT professionals identified insufficient staff and resources as a primary cause of work overload and exhaustion. Implications for practice and future research are discussed.
Reranking and Self-Training for Parser Adaptation
(2006)

Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard “Charniak parser” checks in at a labeled precisionrecall f -measure of 89.7% on the Penn WSJ test set, but only 82.9% on the test set from the Brown treebank corpus. This paper should allay these fears. In particular, we show that the reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2%. Furthermore, use of the self-training techniques described in (McClosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled Brown data. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.4%.
Social network, social trust and shared goals in organizational knowledge sharing
(2008)

The aim of our study was to further develop an understanding of social capital in organizationalknowledge-sharing. We first developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using confirmatory factoring analysis. We then surveyed of 190 managers from Hong Kong firms, we confirm that a social network and shared goals significantly contributed to a person’s volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge. 2008 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +852 3411 7582; fax: +852 3411 5585. E-mail addresses: vwschow@hkbu.edu.hk (W.S. Chow), sherry.ls.chan@gmail.com (L.S. Chan).
Learning A Task-Specific Deep Architecture For Clustering
(2016)

While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graphregularized sparse coding algorithm. It is then trained with taskspecific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.
Vision based fire detection
(2004)

Vision based fire detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based fire detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of fire pixels. The spatial model captures the spatial structure within a fire region. The shape of a fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting fire reliably.
The role of violent media preference in cumulative developmental risk for violence and general aggression.
(2009)

The impact of exposure to violence in the media on the long-term development and short-term expression of aggressive behavior has been well documented. However, gaps in this literature remain, and in particular the role of violent media exposure in shaping violent and other serious antisocial behavior has not been investigated. Further, studies of violent media effects typically have not sampled from populations with confirmed histories of violent and/or nonviolent antisocial behavior. In this study, we analyzed data on 820 youth, including 390 juvenile delinquents and 430 high school students, to examine the relation of violent media use to involvement in violence and general aggression. Using criterion scores developed through cross-informant modeling of data from self, parent/guardian, and teacher/staff reports, we observed that childhood and adolescent violent media preferences contributed significantly to the prediction of violence and general aggression from cumulative risk totals. Findings represent a new and important direction for research on the role of violent media use in the broader matrix of risk factors for youth violence.
Towards Building a SentiWordNet for Tamil
(2016)

Sentiment analysis is a discipline of Natural Language Processing which deals with analysing the subjectivity of the data. It is an important task with both commercial and academic functionality. Languages like English have several resources which assist in the task of sentiment analysis. SentiWordNet for English is one such important lexical resource that contains subjective polarity for each lexical item. With growing data in native vernacular, there is a need for language-specific SentiWordNet(s). In this paper, we discuss a generic approach followed for the development of a Tamil SentiWordNet using currently available resources in English. For Tamil SentiWordNet, a substantial agreement Fleiss Kappa score of 0.663 was obtained after verification from Tamil annotators. Such a resource would serve as a baseline for future improvements in the task of sentiment analysis specific to
Point Cloud GAN
(2018)

Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, instead of using only state-of-the-art Wasserstein GAN objective, we propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form. Thereby, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We validate our claims on ModelNet40 benchmark dataset. Using the distance between generated point clouds and true meshes as metric, we find that PC-GAN trained by the sandwiching objective achieves better results on test data than the existing methods. Moreover, as a byproduct, PCGAN learns versatile latent representations of point clouds, which can achieve competitive performance with other unsupervised learning algorithms on object recognition task. Lastly, we also provide studies on generating unseen classes of objects and transforming image to point cloud, which demonstrates the compelling generalization capability and potentials of PC-GAN.
Understanding the Effects of Mobile Gamification on Learning Performance
(2014)

With the development of mobile technology, mobile learning, which is not bound by time and space, has become one of the most essential learning methods for modern people. However, previous studies have suggested that improvements are still needed in mobile learning performance. One of the means to achieve this goal is situated learning, which enhances learners’ autonomous motivation and make them more enthusiastic in learning. Nonetheless, few studies have attempted to increase learning performance through situated learning. This study attempts to explore the development of a productive learning atmosphere in the context of mobile situated learning. An experiment is conducted with university-level students having homogenous background and coursework by applying heterogeneous pedagogies, including textual pedagogy, video pedagogy, collaborative pedagogy, and gamification pedagogy. Moreover, the uses and gratification theory and the cognitive load theory were adopted to identify the cognitive factors that influence the learning performance in situated learning. It is hoped that education service providers can benefit from the insights discovered from this study and implement more effective learning strategies in mobile education.